Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

Application of chemometrics to analysis of soil pollutants

This overview focuses on the application of chemometrics techniques for the investigation of soils contaminated by polycyclic aromatic hydrocarbons (PAHs) and metals because these two important and very diverse groups of pollutants are ubiquitous in soils. The salient features of various studies carried out in the micro- and recreational environments of humans, are highlighted in the context of the various multivariate statistical techniques available across discipline boundaries that have been effectively used in soil studies. Particular attention is paid to techniques employed in the geosciences that may be effectively utilized for environmental soil studies; classical multivariate approaches that may be used in isolation or as complementary methods to these are also discussed. Chemometrics techniques widely applied in atmospheric studies for identifying sources of pollutants or for determining the importance of contaminant source contributions to a particular site, have seen little use in soil studies, but may be effectively employed in such investigations. Suitable programs are also available for suggesting mitigating measures in cases of soil contamination, and these are also considered. Specific techniques reviewed include pattern recognition techniques such as Principal Components Analysis (PCA), Fuzzy Clustering (FC) and Cluster Analysis (CA); geostatistical tools include variograms, Geographical Information Systems (GIS), contour mapping and kriging; source identification and contribution estimation methods reviewed include Positive Matrix Factorisation (PMF), and Principal Component Analysis on Absolute Principal Component Scores (PCA/APCS). Mitigating measures to limit or eliminate pollutant sources may be suggested through the use of ranking analysis and multi criteria decision making methods (MCDM). These methods are mainly represented in this review by studies employing the Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and its associated graphic output, Geometrical Analysis for Interactive Aid (GAIA).

Strategic project evaluation for open pit mining ventures using real options and allied econometric techniques

Open pit mine operations are complex businesses that demand a constant assessment of risk. This is because the value of a mine project is typically influenced by many underlying economic and physical uncertainties, such as metal prices, metal grades, costs, schedules, quantities, and environmental issues, among others, which are not known with much certainty at the beginning of the project. Hence, mining projects present a considerable challenge to those involved in associated investment decisions, such as the owners of the mine and other stakeholders. In general terms, when an option exists to acquire a new or operating mining project, , the owners and stock holders of the mine project need to know the value of the mining project, which is the fundamental criterion for making final decisions about going ahead with the venture capital. However, obtaining the mine project’s value is not an easy task. The reason for this is that sophisticated valuation and mine optimisation techniques, which combine advanced theories in geostatistics, statistics, engineering, economics and finance, among others, need to be used by the mine analyst or mine planner in order to assess and quantify the existing uncertainty and, consequently, the risk involved in the project investment. Furthermore, current valuation and mine optimisation techniques do not complement each other. That is valuation techniques based on real options (RO) analysis assume an expected (constant) metal grade and ore tonnage during a specified period, while mine optimisation (MO) techniques assume expected (constant) metal prices and mining costs. These assumptions are not totally correct since both sources of uncertainty—that of the orebody (metal grade and reserves of mineral), and that about the future behaviour of metal prices and mining costs—are the ones that have great impact on the value of any mining project. Consequently, the key objective of this thesis is twofold. The first objective consists of analysing and understanding the main sources of uncertainty in an open pit mining project, such as the orebody (in situ metal grade), mining costs and metal price uncertainties, and their effect on the final project value. The second objective consists of breaking down the wall of isolation between economic valuation and mine optimisation techniques in order to generate a novel open pit mine evaluation framework called the ―Integrated Valuation / Optimisation Framework (IVOF)‖. One important characteristic of this new framework is that it incorporates the RO and MO valuation techniques into a single integrated process that quantifies and describes uncertainty and risk in a mine project evaluation process, giving a more realistic estimate of the project’s value. To achieve this, novel and advanced engineering and econometric methods are used to integrate financial and geological uncertainty into dynamic risk forecasting measures. The proposed mine valuation/optimisation technique is then applied to a real gold disseminated open pit mine deposit to estimate its value in the face of orebody, mining costs and metal price uncertainties.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Spatial and temporal analysis of Barmah Forest virus disease in Queensland, Australia

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.

Spatial prediction on a river network

This article develops methods for spatially predicting daily change of dissolved oxygen (Dochange) at both sampled locations (134 freshwater sites in 2002 and 2003) and other locations of interest throughout a river network in South East Queensland, Australia. In order to deal with the relative sparseness of the monitoring locations in comparison to the number of locations where one might want to make predictions, we make a classification of the river and stream locations. We then implement optimal spatial prediction (ordinary and constrained kriging) from geostatistics. Because of their directed-tree structure, rivers and streams offer special challenges. A complete approach to spatial prediction on a river network is given, with special attention paid to environmental exceedances. The methodology is used to produce a map of Dochange predictions for 2003. Dochange is one of the variables measured as part of the Ecosystem Health Monitoring Program conducted within the Moreton Bay Waterways and Catchments Partnership.

Analysis and modelling of the hydraulic conductivity in aquitards : application to the Galilee Basin and the Great Artesian Basin, Australia

In large sedimentary basins with layers of different rocks, the groundwater flow between aquifers depends on the hydraulic conductivity (K) of the separating low-permeable rocks, or aquitards. Three methods were developed to evaluate K in aquitards for areas with limited field data: • Coherence and harmonic analysis: estimates the regional-scale K based on water-level fluctuations in adjacent aquifers. • Cokriging and Bayes' rule: infers K from downhole geophysical logs. • Fluvial process model: reproduces the lithology architecture of sediment formations which can be converted to K. These proposed methods enable good estimates of K and better planning of further drillholes.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

用地质统计学计算土层相关尺度

随机场理论可以用来模拟土工参数的空间变异性，而地质统计学也借助于随机场理论对自然资源进行估计、模拟和评价，很明显随机场理论是两类应用的共同理论基础，因而借助于发展较成熟的地质统计学理论估算土层相关尺度应是一条有效的途径。本文论述了该方法的理论基础，认为求取变差函数与求相关函数是等价的，建立并证明了八类理论变差函数与其相应的相关距离之间的解析关系，介绍了具体的计算步骤：计算实验变差函数、选择合适的理论变差函数模型、最优拟合实验变差函数、计算相关距离，以实例说明了实际步骤以及结果的有效性和实用性。

Water Content Distribution in the Surface Layer of Maoping Slope

The water content distribution in the surface layer of Maoping slope has been studied by testing the water content at 31 control sites. The water content profiles at these sites have also been determined. The water content distributions at different segments have been obtained by using the Kriging method of geostatistics. By comparing the water content distributions with the landform of the slope, it was shown that the water content is closely dependent on the landform of the slope. The water content distribution in the surface layer provided a fundamental basis for landslide predication and treatment.

Estimating exploitable stock biomass for the Maine green sea urchin (Strongylocentrotus droebachiensis) fishery using a spatial statistics approach

The objective of this study was to investigate the spatial patterns in green sea urchin (Strongylocentrotus droebachiensis) density off the coast of Maine, using data from a fishery-independent survey program, to estimate the exploitable biomass of this species. The dependence of sea urchin variables on the environment, the lack of stationarity, and the presence of discontinuities in the study area made intrinsic geostatistics inappropriate for the study; therefore, we used triangulated irregular networks (TINs) to characterize the large-scale patterns in sea urchin density. The resulting density surfaces were modified to include only areas of the appropriate substrate type and depth zone, and were used to calculate total biomass. Exploitable biomass was estimated by using two different sea urchin density threshold values, which made different assumptions about the fishing industry. We observed considerable spatial variability on both small and large scales, including large-scale patterns in sea urchin density related to depth and fishing pressure. We conclude that the TIN method provides a reasonable spatial approach for generating biomass estimates for a fishery unsuited to geostatistics, but we suggest further studies into uncertainty estimation and the selection of threshold density values.

存在一般性的生物多样性垂直格局理论吗？

　是否存在一般性的生物多样性垂直格局理论，是人们普遍关心的问题。为了解决这一疑问。本文设计了三个方面的问题作为研究的内容。一、标准化系统取样能够满足山地生物多样性研究的需要吗？二、山地植物群落中不同植物类群的物种丰富度沿海拔梯度有相同的格局变化吗？三、山地植物群落中，不同植物类群的群落水平多样性，包括物种丰富度、均匀度和群落多样性沿海拔梯度有怎样的变化？不同植物类群内，及不同植物类群间物种丰富度、物种均匀度和群落多样性之间的空间相互关系？ 为了解决上述问题，我们采用了模拟和试验两类数据（来自北京东灵山地区7条垂直样带），研究中着重考虑了数据的空间相关性或尺度依赖性，应用地统计学和多元地统计学的方法对数据进行分析，得出如下结论： 一、标准化系统取样是不能满足山地物种多样性研究的需要的，它忽略了取样单元面积效应。理论上，文献所列的四种典型的生物多样性格局类型都可以随取样单元而相互转换;评价生物多样性垂直格局时，不存在唯一合适的取样单元尺度。通常在一定尺度范围内，多样性显现某一格局类型，超过了尺度阀值，格局类型会发生转换，但是存在一个最小取样单元尺度，小于这一尺度生物多样性的空间结构将不能正确显现;山地生物多样性分布具各向异性，垂直方向物种替代速率远大于水平方向，因此，不但取样面积，而且取样单元的形状也对多样性格局有重要影响。山地生物多样性格局研究中，以垂直方向作长边的长方形较为合适;本研究没有对物种多样性格局随取样单元发生转换的决定因素得出明确的结论，但 多样性的空间分布可能是一主要因素。 二、乔、灌、草和群落总的物种丰富度格局，大多数大尺度上呈现梯度格局，小尺度上呈斑块的聚集。物种丰富度垂直格局的尺度依赖性与坡向有密切关系，同一坡向的格局的特征表现出很强的一致性;乔、灌、草及总的植物物种丰富度垂直梯度格局与取样单元尺度的关系也表现出与坡向的密切关系，同一坡向合适的取样面积基本一致;总的来说，不同植物类群的物种丰富度垂直格局都表现出其独特的特点，不同坡向同一植物类群又表现出明显不同的特点。将我们的研究结果与其它山地物种丰富度的垂直格局研究进行比较，得出这样的推论：在山地植物多样性研究中，同一群落层次在相同的生物地理区（或气候带）内，物种丰富度具有一致的垂直格局类型，而在不同的生物地理区内具有不同的特点。分别研究不同群落层次物种丰富度在同一生物地理区不同地点，以及在不同生物地理区的垂直格局，有利于区别历史、进化因素和现时生态因素对生物多样性形成和维持的作用，形成一般的生物多样性理论。 三、乔、灌、草三层的群落多样性Simpson指数和物种丰富的垂直格局类型基本一致，多样性和丰富度格局的相似性不但表现在格局的类型上，而且表现在对空间的尺度依赖上，如自相关范围、大尺度上梯度效应等都表现出了一致性;均匀度格局变化比较复杂，在乔木层、阴坡灌木层与物种丰富度有相似的格局特征，阳坡灌木层均匀度则基本不随海拔变化;草本层均匀度很高，表现出与物种丰富度、多样性相反的格局。我们认为物种丰富度、多样性、均匀度沿着环境梯度变化以及它们之间的关系还不能确定是否有一般性特征，它们可能不但与生物类群而且与垂直样带所处的环境有关。理论研究认为在大尺度上群落多样性的变化主要反映在物种丰富度水平上的变化，主要由均匀度引起的多样性的变化可能只存在小范围的环境梯度中，本文的研究为这一理论提供了佐证。理论模拟表明物种丰富度、均匀度和多样性之间是简单的强正相关性，丰富度和均匀度相互独立存在，我们证明了它们之间并非简单的正相关，丰富度和均匀度也非简单独立存在，它们的关系与尺度、坡向（环境条件）、植物类群密切相关，不同的生态过程或因素在不同尺度上的作用决定了它们的关系类型。乔、灌层间各多样性测度间总体呈正相关关系，并随尺度增加而降低;乔、草层间多样性测度间的关系与乔、灌层间明显不同，乔木层各多样性测度与草本层丰富度、多样性指数间显著负相关，与草本层均匀度间弱正相关，并随尺度增大渐弱;灌、草层间与乔、草层间情形相似，特别是阴坡。不同层次间多样性测度的相互关系还与坡向和尺度密切相关。 通过本研究，虽然不能肯定回答是否存在一般性的生物多样性垂直格局理论，但是我们认为在同一生物地理区域内，对同一生物类群（具有相同或相似的生物、生态学特性，具有对外界环境相同或相似的适应方式），并匹配于适宜的尺度揭示生物多样性垂直格局，并比较不同生物地理区、不同生物类群的生物多样性垂直格局，可能会找出一般性的生物多样性垂直格局理论。

三角洲储层孔隙结构模型与剩余油分布研究——以临南油田沙河街组储层为例

The study of pore structure in reservoir was paid attention to in the early reservoir research and now a systematic research methodology is set up. On the limits of tools and conditions, methodologies and technologies on formation condition and distribution laws of pore structure and the relationship between remaining oil distribution and pore structure are uncertain and some knownage about it is also uncertain. As the development of petroleum industry, the characterization of pore structure and the prediction of remaining oil are the hot spot and difficult point in the research of oil development. The author pays a close attention to this subject and has done much research on it. In a case study in Linnan oilfield Huimin sag Jiyang Depression Bohai Bay basin by using a new method, named varied scale comprehensive modeling of pore structure, the author builds pore structure models for delta reservoir, reveals the remaining oil distribution laws in delta facies, and predicts the distribution of remaining oil in Linnan oilfield. By the application of stratigraphy, sedimentology and structure geology. the author reveals the genetic types of sandbody and its distribution laws, builds the reservoir geological models for delta sandstone reservoir in Shahejie group in Linnan oilfield and points out the geological Factors that control the development of pores and throats. Combining petrology and the reservoir sensitive analysis, the author builds the rock matrix models. It is the first time to state that rocks in different sentimental micro facies have different sensitive .response to fluid pressed into the rocks. Normally. the reservoirs in the delta front have weaker sensitivity to fluid than the reservoirs in delta plain, In same subfacies, the microfacies that have fine grain, such as bank and crevasse splay, have stronger reservoir sensitivity than the microfacies that have coarse grains, such as under-water branched channel and debauch bar. By the application of advanced testing, such as imagine analysis, scan electronic microscope, and morphology method, the author classifies the pore structure and set up the distribution models of pore, throat and pore structure. By the application of advanced theory in well-logging geology, the author finds the relationship between microscope pore structure and macroscopic percolation characteristics, and then builds the well-logging interpretation formulae for calculating pore structure parameters. By using the geostatistics methods, the author reveals the spatial correlative characteristics of pore structure. By application of conditional stochastic simulation methods, the author builds the 3D models of pore structure in delta reservoir. It is the base of predicting remaining oil distribution. By a great deal of experiments and theoretical deduction, The author expounds the laws of percolation flow in different pore structures, and the laws by which the pore structure controls the micro distribution of remaining oil, and then, states the micro mechanism of remaining oil distribution. There are two types of remaining oil. They are by-pass flow caused by micro-fingering and truncation caused by non-piston movement. By new method, the author states the different pore structure has different replacement efficiency, reveals the formation condition and distribution laws of remaining oil. predicts the remaining oil distribution in Linnan oil field, and put forward some idea about how to adjust the oil production. The study yielded good results in the production in Linnan oilfield.

Non-stationary variogram models for geostatistical sampling optimisation: An empirical investigation using elevation data

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.